Microservice Asset Validation Test Generation for Shared Decision Services
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Solution Overview
Problem
Existing systems face inefficiencies in creating and testing common data assets shared among decision services, leading to redundant manual efforts and potential errors due to the lack of automated asset validation testing, which is time-consuming and resource-intensive.
Innovation Solution
An asset validation framework that uses machine learning and AI models to automatically generate and adapt common asset validation tests across different decision services, reducing manual efforts and enhancing testing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual asset validation testing is performed for each decision service, then testing coverage can be ensured, but significant time and resources are required
Solution Approach 1:
The system copies existing asset validation tests from one decision service to another when the same asset is used across multiple services. Instead of manually creating duplicate tests for each service, the framework automatically replicates and adapts tests from source services to target services, ensuring consistent validation coverage while eliminating redundant manual testing efforts
Solution Approach 2:
The system enables self-service automated test generation where the asset validation framework automatically creates, adapts, and executes validation tests without requiring manual intervention from developers or testers. The framework autonomously identifies assets used by decision services and generates appropriate validation tests, freeing human resources from repetitive manual testing tasks
2Productivity
If automated asset validation testing is implemented, then resource usage is reduced, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary asset validation framework that sits between the decision services and the testing infrastructure. This framework acts as a mediator that automatically manages test generation, adaptation, and execution, handling the complexity of automated testing while presenting a simplified interface to users and developers
Solution Approach 2:
The asset validation framework is designed as a universal system that can validate assets across multiple decision services with a single implementation. It provides multi-functional capabilities including test generation, test adaptation, test execution, and result aggregation, eliminating the need for separate testing mechanisms for each service and reducing overall system complexity
3Productivity
If common assets are shared between multiple decision services, then efficiency is improved, but validation testing becomes more difficult
Solution Approach 1:
The system merges validation testing for common assets shared across multiple decision services into a unified testing approach. Instead of maintaining separate validation tests for each service using the same asset, the framework consolidates test definitions and executes them once, with results automatically applied to all services utilizing the shared asset, thereby simplifying validation while maintaining efficiency
Data Source
AI summary
There are provided systems and methods for automatic generation of common asset validation tests for platform-based microservices. A service provider, such as an electronic transaction processor for digital transactions, may utilize different decision services that implement rules or artificial intelligence models for decision-making from data in a production computing environment. A decision service may normally be used for data processing and decision-making through an execution flow configuration or graph identifying a flow of task executions and other computing operations. In this regard, the decision services may share common data assets, such as data tables, shared code for execution of operations and the like. The service provider may utilize an intelligent service to automatically generate tests that validate the common assets for usage by and deployment with decision services. The tests may be generated from existing tests by reconfiguring for test requirement parameters of each decision service.